Large amounts of capital are spent every day drilling, evaluating, testing and completing new oil and gas wells. The elimination of unnecessary data gathering and the gathering of necessary additional information is often the subject of intense disagreements when decisions are being made throughout the drilling/exploration process. The tendency is to overpurchase new technologies with the assumption that a better answer can be reached.
The identification and evaluation of hydrocarbon productive intervals such as oil and gas reservoirs in a formation traversed by a well bore or borehole have historically been done by lowering instruments into a well and measuring petrophysical parameters such as formation resistivity and density. During the drilling, borehole samples from the formation are collected by a process called core sampling. These samples are then analyzed in laboratories and various parameters are measured to determine petrophysical properties.
The results of these measurements are then numerically processed using empirical relationships in order to calculate water saturation, porosity and permeability which describe key formation properties. These variables are key indicators of hydrocarbon volume and hydrocarbon productivity, respectively. Based on these values, petrophysicists use their experience to make a judgment and to determine the potential presence of commercial hydrocarbons.
One of the main problems with the evaluation of hydrocarbon productivity of an oil or gas well is the amount of uncertainty that exists in the measurements used to make the determination as well as the variability in the rock formations where oil and gas is found. Formation heterogeneity errors, tool measurement errors and laboratory measurement errors create uncertainties that are often difficult to determine at every depth interval of the measured values. In addition, errors in the measurements and the parameters used in the empirical relationships are carried forward and unaccounted for in the final solution. These results are then used to determine the best course of action for the well i.e. whether to test, complete or plug the well.
Accounting for these facts has been left to interpreters/petrophysicists that are seen as both scientists and artists, using empirical relationships derived from guesses and rules of thumb. The nuances in measurements are considered by the expert and a prediction based on local experience is relied upon such that these predictions have resulted in missed opportunities for every major operating company in the petrochemical field. Previous methodologies did not consider uncertainty, as all assessments were either positive or negative.
A known system, described in U.S. Pat. No. 4,338,664, was developed to forward model logging tool responses resulting in predicted logs. The differences in the predicted results were minimized through an iterative process. Ultimately the final solution to the iterative process results in a formation description that includes porosity and water saturation.
The saturation is determined from a user-selected empirical-based equations using the optimized results.
Systems and methods in the prior art do not account for uncertainty in all parameters and measurements as well as the heterogeneity in hydrocarbon bearing formations.
Methods called Monte Carlo methods are known which include a class of computational algorithms for simulating the behavior of various physical and mathematical systems. Such Monte Carlo methods are distinguished from other simulation methods by being stochastic, i.e., non-deterministic in some manner, usually by using random numbers or pseudo-random numbers, as opposed to deterministic algorithms.
Monte Carlo methods are extremely important in computational physics and related applied fields, and have diverse applications. These methods have proven efficient in solving difficult problems in various fields. They are especially useful in studying systems with a large number of coupled degrees of freedom, such as liquids, disordered materials, and strongly coupled solids. Because of the repetition of algorithms and the large number of calculations involved, Monte Carlo methods are suited to calculation using a computer, utilizing many techniques of computer simulation.
It is recognized that the amount of uncertainty provides an avenue to explain an incorrect prediction. Quantification of the uncertainty boundaries by forward modeling utilizing Monte Carlo simulations overcomes these limitations.
The present invention aims to directly address the variables to enable those skilled in the art to state what is and what is not certain. The invention comprehends a system and method which employ Monte Carlo simulations to analyze hydrocarbon zones to replace the use of empirical relationships to obtain improved productivity.